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Updated: Feb 28, 2026
Four-Dimensional CT Analysis Using Sequential 3D-3D Registration
Published on: November 23, 2019
Validation of static and dynamic radiostereometric analysis of the knee joint using bone models from CT data
K Stentz-Olesen1, E T Nielsen1, S De Raedt2
1Orthopaedic Research Group, Department of Orthopaedic Surgery, Aarhus University Hospital, Tage-Hansens Gade 2, Building 10A, Office 15, 8000 Aarhus C, Denmark.
Objectives:
Static radiostereometric analysis (RSA) using implanted markers is considered the most accurate system for the evaluation of prosthesis migration. By using CT bone models instead of markers, combined with a dynamic RSA system, a non-invasive measurement of joint movement is enabled. This method is more accurate than current 3D skin marker-based tracking systems. The purpose of this study was to evaluate the accuracy of the CT model method for measuring knee joint kinematics in static and dynamic RSA using the marker method as the benchmark.
Methods:
Bone models were created from CT scans, and tantalum beads were implanted into the tibia and femur of eight human cadaver knees. Each specimen was secured in a fixture, static and dynamic stereoradiographs were recorded, and the bone models and marker models were fitted to the stereoradiographs.
Results:
Results showed a mean difference between the two methods in all six degrees of freedom for static RSA to be within -0.10 mm/° and 0.08 mm/° with a 95% limit of agreement (LoA) ranging from ± 0.49 to 1.26. Dynamic RSA had a slightly larger range in mean difference of -0.23 mm/° to 0.16 mm/° with LoA ranging from ± 0.75 to 1.50.
Conclusions:
In a laboratory-controlled setting, the CT model method combined with dynamic RSA may be an alternative to previous marker-based methods for kinematic analyses.Cite this article: K. Stentz-Olesen, E. T. Nielsen, S. De Raedt, P. B. Jørgensen, O. G. Sørensen, B. L. Kaptein, M. S. Andersen, M. Stilling. Validation of static and dynamic radiostereometric analysis of the knee joint using bone models from CT data. Bone Joint Res 2017;6:376-384. DOI: 10.1302/2046-3758.66.BJR-2016-0113.R3.
